I. Field of the Invention
The present invention relates generally to a system and method for object recognition and, more particularly, to such a method and system using a neural network.
II. Description of Related Art
There are many previously known systems for object recognition. Such systems typically utilize a camera to take a two-dimensional view of the object and then process the data in that view in an attempt to identify the object or the position of the object. Such systems oftentimes use one or more microprocessors to process the data.
Although the processing capabilities of microprocessors have increased dramatically in recent years, the processing of the image captured by the camera or other suitable sensor for these previously known object recognition systems is still computationally demanding.
There have been previously known systems which utilize a neural network to process the image captured by the camera or other suitable sensor in such object recognition systems. The image is usually fed into the algorithm as a collection of features, e.g., pixel intensities. The temporal order of such features is meaningless in the context of the single image. More importantly, the number of features can be very large, making the task of object recognition computationally very demanding.
A still further shortcoming of these previously known object recognition systems is that such systems were unable to perform reliable rotational invariant recognition of objects. In many situations, however, the precise orientation of the object desired to be recognized is unknown and will vary from one object to the next, making the object recognition a very challenging task. That is also true where object recognition is employed in an automotive vehicle for objects outside the vehicle.